ResNet101
PulseAugur coverage of ResNet101 — every cluster mentioning ResNet101 across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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AI models achieve high accuracy in retinal disease classification and vessel segmentation
Researchers have developed a novel two-pipeline framework for analyzing retinal fundus images, combining disease classification with blood vessel segmentation. The framework fine-tuned eight ImageNet-pretrained CNNs for…
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New Vision Transformer Enhances Plant Trait Recognition in Herbarium Images
Researchers have developed AT-ViT, a novel dual-branch Vision Transformer designed to improve plant trait recognition from herbarium images. This model addresses the challenge of background noise and spurious correlatio…
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New LaryngealCT Dataset Benchmarks Deep Learning for Cancer Staging
Researchers have developed LaryngealCT, a new benchmark dataset for staging laryngeal cancer using deep learning models. The dataset comprises 1,029 CT scans aggregated from The Cancer Imaging Archive and has been used …
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CNNs achieve 90% accuracy classifying galaxy images
Researchers have evaluated the performance of ResNet101 and InceptionV4 convolutional neural networks for classifying galaxy images. Both models achieved approximately 90% accuracy on the Galaxy10 DECals dataset, demons…
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TDA-ViT model fuses topology and transformers for 99% brain tumor classification
Researchers have developed a novel fusion model that combines Topological Data Analysis (TDA) with Vision Transformers (ViTs) for improved brain tumor classification from MRI scans. This TDA-ViT model extracts both geom…
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New DEEP-GAP study compares NVIDIA T4 and L4 GPU inference performance
A new research paper introduces DEEP-GAP, a methodology for evaluating GPU inference performance. The study systematically compares the NVIDIA T4 and L4 GPUs using various deep learning models and precision modes. Resul…
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Researchers combine DPUs and GPUs for faster neural network inference
Researchers have developed a novel method for accelerating neural network inference by splitting Convolutional Neural Network (CNN) computations between Deep Learning Processing Units (DPUs) and Graphics Processing Unit…